Forecasting macrophage activation syndromes

ABSTRACT

Technologies are disclosed for determining or predicting the occurrence of a macrophage activation syndrome, such as hemophagocytic lymphohistiocytosis (HLH). A detection of the emergence of and/or a reliable estimation of the likelihood of future significant macrophage activation syndromes, such as HLH, may be determined or predicted from a time series of laboratory and physiologic values to be measured in a patient. Root mean square of successive deviations (RMSSD) is utilized as a surrogate non-parametric measure of the high-frequency power spectral density (PSD) to identify strong statistical associations with the presence and/or near-term future emergence of macrophage activation syndromes. Utilizing these input variables, a model having satisfactory predictive accuracy is constructed using linear discriminant analysis (LDA), gradient boosting, random forest (RF), neural network, logistic regression, or the like, and may be used for the prediction.

CROSS-REFERENCES TO RELATED APPLICATIONS

This application claims priority to U.S. patent application Ser. No.15/717,777, filed Sep. 27, 2017, entitled “Forecasting MacrophageActivation Syndromes,” and U.S. Provisional Patent Application No.62/401,843, filed Sep. 29, 2016, entitled “Forecasting MacrophageActivation Syndromes.” The entire contents of each are herebyincorporated by reference for all purposes.

BACKGROUND

Hemophagocytic lymphohistiocytosis (HLH) is a life-threatening syndromeof immune dysregulation and is classified as primary or secondaryaccording to the underlying etiology. Reactive hemophagocytic syndromeis triggered by various infections, hematologic malignancies, andsystemic autoimmune diseases (JRA, IM) and is contrasted with familialHLH. The latter condition is associated with mutations in the performgene related to T-cell cytotoxicity; both conditions are associated withhypersecretion of pro-inflammatory and Th1 cytokines, includingIFN-gamma, IL-1, IL-6, IL-18, and TNF-alpha. In humans, mutations inseven genes encoding proteins involved in cytolytic effector functionshave so far been identified that predispose to HLH. However, althoughmost affected patients develop HLH eventually, disease onset andseverity are highly variable. Due to the genetic heterogeneity andvariable time and nature of disease triggers, the immunological basis ofthese variations in HLH progression is incompletely understood. Acquiredor ‘reactive’ HLH is related to hematologic malignancy in 72%,infectious disease in 15%, autoimmune disease in 12%, and ismulti-factorial in approximately 11% of patients.

Organ dysfunction besides hematologic dysfunction is eventually presentin all patients. In many, multi-organ system dysfunction (MOD)eventuates. Hepatic and/or respiratory failure requiring mechanicalventilation are very common (>80%), and in many cases cardiovascularfailure and shock supervene (70%). Many manifest neurologic dysfunctionincluding convulsion and coma, and renal failure is present as well,commonly following hemodynamic decompensation or shock. The conditionhas mortality rates between 18% and 60%. Most deaths occur between 6 and26 weeks following the onset of the HLH syndrome. Thus, there istypically considerable time during which to establish the diagnosis andundertake immunomodulatory and other treatments. While there have beenattempts to provide a technological solution, technology has largelyfailed to provide a reliable and accurate solution.

SUMMARY

This summary is provided to introduce a selection of concepts in asimplified form that are further described below in the detaileddescription. This summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used in isolation as an aid in determining the scope of the claimedsubject matter.

Embodiments described in this disclosure are directed towards systemsand methods for determining or predicting the occurrence of a macrophageactivation syndrome, such as hemophagocytic lymphohistiocytosis (HLH).For example, in an embodiment a detection of the emergence of and/or areliable estimation of the likelihood of future significant macrophageactivation syndromes, such as HLH, may be determined or predicted from atime series of laboratory and physiologic values to be measured in apatient. Further, in some embodiments, the time horizon of the futuretime interval predicted may range from 72 hours to approximately 30days, depending on the frequency of laboratory measurements. Inparticular, embodiments utilize spectrum-analytic andinformation-theoretic methods that do not require that the measurementsbe acquired on an especially frequent or regular or periodic basis.Rather, embodiments take advantage of these short- and longer-rangepatterns in “regularly irregular” phenomena from inconsistencies intimes of measuring laboratory and physiologic parameters to producerobust forecasts of near-term risk of hypoglycemia and hyperglycemia.

In one aspect, root mean square of successive deviations (RMSSD) isutilized as a surrogate non-parametric measure of the high-frequencypower spectral density (PSD) to identify strong statistical associationswith the presence and/or near-term future emergence of macrophageactivation syndromes. Utilizing these input variables, a models havingsatisfactory predictive accuracy may be constructed using lineardiscriminant analysis (LDA), gradient boosting, random forest (RF),neural network, logistic regression, and/or other, similarclassification or regression models. In this way, embodiments of thedisclosure are robust against intermittent gaps or failures on the partof the patient or caregiver to perform usual measurements, againstdelays in uploading or sync'ing newly acquired values with historicaltime series measured in the patient, and against non-stationarity in thetime series such as may arise during periods when the patient's healthdeviates from predominant patterns, such as when the patient has anacute infection.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is described in detail below with reference to theattached drawing figures, wherein:

FIGS. 1A and 1B depict aspects of an illustrative architecture suitablefor practicing an embodiment of the invention;

FIGS. 2A and 2B depict a flow diagram of an exemplary method forgenerating one or more gradient boosting models for use in determiningor predicting the occurrence of a macrophage activation syndrome, inaccordance with an embodiment of this disclosure;

FIGS. 3A-3D depict statistical distributions of values of the root meansquare of successive differences for a 3-sample-wide time series for oneof the laboratory parameters used for determining predictive models forHLH, in an embodiment of this technology reduced to practice;

FIGS. 4A-4D depict root mean square of successive deviations (RMSSD)distributions from an embodiment of this technology reduced to practice;

FIG. 5A depicts max shock index (SI) distribution in HLH patient, inaccordance with an embodiment of this disclosure;

FIG. 5B depicts the number of threshold transgressions as a function ofthe max SI index, in accordance with an embodiment of this disclosure;

FIGS. 5C and 5D depict a Q-Q plot of copula regression and the accuracyof the predicted threshold transgressions, respectively, in accordancewith an embodiment of this disclosure;

FIG. 6A illustratively provide an example embodiment of a computerprogram routine for performing the HLH copula regression utilized todetermining or predicting the occurrence of a macrophage activationsyndrome; and

FIG. 6B depicts a look-up table of probabilities utilized by the examplepredictive model, in accordance with an embodiment of this disclosure.

DETAILED DESCRIPTION

The subject matter of the present invention is described withspecificity herein to meet statutory requirements. However, thedescription itself is not intended to limit the scope of this patent.Rather, the inventors have contemplated that the claimed subject mattermight also be embodied in other ways, to include different steps orcombinations of steps similar to the ones described in this document, inconjunction with other present or future technologies. Moreover,although the terms “step” and/or “block” may be used herein to connotedifferent elements of methods employed, the terms should not beinterpreted as implying any particular order among or between varioussteps herein disclosed unless and except when the order of individualsteps is explicitly described.

As one skilled in the art will appreciate, embodiments of our inventionmay be embodied as, among other things: a method, system, or set ofinstructions embodied on one or more computer readable media.Accordingly, the embodiments may take the form of a hardware embodiment,a software embodiment, or an embodiment combining software and hardware.In one embodiment, the invention takes the form of a computer-programproduct that includes computer-usable instructions embodied on one ormore computer readable media.

Computer-readable media can be any available media that can be accessedby a computing device and includes both volatile and nonvolatile media,removable and non-removable media. By way of example, and notlimitation, computer-readable media comprises media implemented in anymethod or technology for storing information, including computer-storagemedia and communications media. Computer storage media includes bothvolatile and nonvolatile, removable and non-removable media implementedin any method or technology for storage of information such ascomputer-readable instructions, data structures, program modules orother data. Computer storage media includes, but is not limited to, RAM,ROM, EEPROM, flash memory or other memory technology, CD-ROM, digitalversatile disks (DVD) or other optical disk storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or any other medium which can be used to store the desired informationand which can be accessed by computing device 100. Computer storagemedia does not comprise signals per se. Communication media typicallyembodies computer-readable instructions, data structures, programmodules or other data in a modulated data signal such as a carrier waveor other transport mechanism and includes any information deliverymedia. The term “modulated data signal” means a signal that has one ormore of its characteristics set or changed in such a manner as to encodeinformation in the signal. By way of example, and not limitation,communication media includes wired media such as a wired network ordirect-wired connection, and wireless media such as acoustic, RF,infrared and other wireless media. Combinations of any of the aboveshould also be included within the scope of computer-readable media.

As described above, system, methods, and computer-readable media areprovided herein for early detection of the emergence of and reliableestimation of the likelihood of future significant macrophage activationsyndromes, such as HLH. The treatment strategies recommended foracquired vs. familial groups differ substantially. Usually treatment isinitiated with dexamethasone 10 mg IV bid, cyclosporine 100 mg IV twicedaily, anakinra 100 mg sc once daily, 1 dose of etoposide 100 mg IV, andoptionally 3 rituximab infusions (range 1-10) at a median dose of 375mg/m2, intravenous immunoglobulin (IVIG), and transfusions of bloodproducts as needed to address the patient's cytopenias. To date, it isthought to be impossible to predict the HLH using conventionallaboratory tests. HLH is characterized by excessive macrophage andT-cell activation as well as impairment of the ability of natural killer(NK) and cytotoxic T cells to kill target cells. Uncontrolled activationof T cells produces excess IL-2, tumor necrosis factor-alpha, andinterferon-gamma. Recent studies show that serum levels of solubleinterleukin-2 receptor (sIL2R) and ferritin are useful fordifferentiating some forms of HLH. However, these laboratory tests arenot broadly available and are relatively expensive to perform even inthose locations where they are available.

The main clinical features of HLH include high fever,hepatosplenomegaly, cytopenia of two or three of the cell lines in thebone marrow, and the presence of activated macrophages in hematopoieticsites; these constitute the classical HLH criteria. Among the twohemophagocytic syndrome subsets, primary/genetic or secondary/reactive,the latter is the most frequently seen in adults. This condition may betriggered by various underlying conditions such as infection (especiallyby Epstein-Barr virus (EBV), cytomegalovirus (CMV), or humanimmunodeficiency virus (HIV)), lymphoid malignancy (B- or T-celllymphoma), connective tissue diseases, or some drugs. HLH criteria wereestablished by the Histiocyte Society in 2004.

More recently, some have proposed an “HScore,” a multi-variablediagnostic tool. Nine variables (3 clinical [i.e., known underlyingimmunosuppression, high temperature, organomegaly], 5 biologic [i.e.,triglyceride, ferritin, serum glutamic oxaloacetic transaminase, andfibrinogen levels, cytopenia], and cytologic attributes [e.g.,hemophagocytosis features on bone marrow aspirate]) are variables in theHScore. However, HScore and other conventional diagnostic methods are“lagging” indicators and do not positively identify HLH until it iswell-established and relatively severe. Combinations of commonlaboratory parameters, such as the percentage of lymphocytes in theleukocyte differential cell count, elevated lactate dehydrogenase (LDH),elevated serum ferritin levels higher than 50,000 μg/L, and thesIL2R/ferritin ratio are useful for identifying patients with familialhaemophagocytic lymphohistiocytosis and for differentiating theunderlying etiology of pediatric HLH.

We have determined progressive pancytopenia is the feature most likelyto suggest secondary HLH in the patients who develop shock and/or organsystem failure. Use of other HLH-2004 diagnostic criteria is hindered bythe poor operating characteristics of these tests in critically illpatients. Their usefulness is confounded by the facts that (a) treatingphysicians' therapeutic maneuvers substantially normalize many of thevariables that are part of the HLH-2004 criteria, (b) necessarytreatments for an underlying condition such as cancer producesabnormalities meeting the HLH-2004 criteria in a percentage of personswho do not later develop HLH, and (c) intercurrent infection or othercomorbid conditions may partially mimic the features of macrophageactivation syndromes, including HLH. Physical examination forsplenomegaly and/or hepatomegaly similarly has poor statisticalperformance in terms of predictive accuracy. Bone marrow aspiration ispresently the most useful diagnostic test, but frequently yieldsfalse-negative results. Additionally, due to its pain and invasiveness,bone marrow aspiration is not generally useful for longitudinaldetermination of progression or severity of macrophage activation andhemophagocytic syndromes.

For the foregoing reasons, the improved systems and methods provided byembodiments of this disclosure for ascertaining the presence of amacrophage activation syndrome and determining or predicting itsseverity in terms of acute probability of deterioration or organ systemfailure are valuable and needed. Further, the application of theembodiments described herein lead to a significant improvement inpatient outcomes, clinical support systems, and the health-care industryin general.

To achieve this improved system a first aspect described herein isdirected to a method for predicting the occurrence of a macrophageactivation syndrome in a human patient over a future time interval. Themethod includes receiving laboratory or physiologic measurement(s) ofthe patient and corresponding date-time coordinate(s), receivingprevious measurement values and their respective measurement date-timecoordinates, and receiving one or more predictive models previouslydetermined from a population of patients in whom subsequent actualoccurrences of a macrophage activation syndrome in a defined future timeinterval were known. Further, some embodiments include constructing atime series by appending the most recent value(s) to the previous seriesof measurements and determining that the length of the time series is atleast three measurement members. For each member of the time series, thehigh-frequency components of a power spectrum or a surrogate measure ofthe high-frequency band of the power spectrum are calculated, therebyforming a set of parameters. A probability of a macrophage activationsyndrome for the patient is determined by applying the parameters asinputs to the one or more predictive models predictive models. Acalculated classification or probability of future macrophage activationsyndrome occurrence within a future time interval is then determiningfor the patient. The calculated classification or probability iscompared against a threshold and when the probability or classificationexceeds the threshold the method emits a notification to a caregiverassociated with the patient indicating the likelihood of an occurrenceof a macrophage activation syndrome in a human patient over the futuretime interval.

A second aspect described herein is directed to a system for predictingthe occurrence of a macrophage activation syndrome in a patient. Someembodiments of the system include an input means for entering at leastone serial laboratory or physiologic measurements and at least oneprocessing means communicatively coupled to the input means. Theprocessing means may be configured to receive one or more laboratory orphysiologic measurements or a patient and associated date-timecoordinates, receive previous measurement values and their associateddate-time coordinates, receive one or more predictive models previouslydetermined from a population of patients in whom subsequent actualoccurrences of a macrophage activation syndrome in a defined future timeinterval were known, construct at least one time series of the one ormore laboratory or physiologic measurements, wherein the at least onetime series comprises at least three measurement values and associateddate-time coordinates, calculate, for each member of the at least onetime series, high-frequency components of a power spectrum or asurrogate measure of the high-frequency band of the power spectrumthereby forming a set of parameters, determine a probability of amacrophage activation syndrome for the patient by applying theparameters as inputs to the one or more predictive models predictivemodels, wherein a generated output comprises a probability orclassification, compare the calculated classification or probabilityagainst a threshold, determine that the probability or classificationsatisfies the threshold, and preform an action. In some embodiments theaction may comprise at least one of notifying a responsible careprovider, placing a new order in the patient's EMR, altering thepatients care plan, reserving a resource, for the patient, in a carefacility for treating macrophage activation disorder(s), modifying aplan of care for the patient that prevents interrupts, or delaysdischarge orders from being submitted or carried out, automaticallyscheduling increased monitoring of the patient; ordering increasedtesting of the patient, automatically scheduling a consultation with aspecialist care provider, automatically issuing a clinical order for thepatient, and issuing an electronic alert or notification to theresponsible care provider or patient.

A third aspect described herein is directed to one or more computerstorage media storing computer-useable instruction that, whenimplemented on a computing device, cause the computing device to performoperations. In some embodiments the operations may comprise receivingone or more laboratory and/or physiologic measurements of a patient andassociated date-time coordinates; receiving previous measurement valuesand their associated date-time coordinates; receiving one or morepredictive models previously determined from a population of patients inwhom subsequent actual occurrences of a macrophage activation syndromein a defined future time interval were known; constructing at least onetime series of the one or more laboratory and/or physiologicmeasurement(s) by appending the recent measurement(s) to the previousmeasurement(s), wherein the at least one time series comprises at leastthree measurement values and associated date-time coordinates;calculating, for each member of the at least one time series,high-frequency components of a power spectrum or a surrogate measure ofthe high-frequency band of the power spectrum thereby forming a set ofparameters; determining a probability of a macrophage activationsyndrome for the patient by applying the parameters as inputs to the oneor more predictive models predictive models, wherein a generated outputcomprises a probability or classification; comparing the calculatedclassification or probability against one or more thresholds; evoking anaction corresponding to preparing for treatment of the patient based oncomparing the probability to the one or more thresholds. In someembodiments, the action may be at least one of: notifying theresponsible care provider; placing new order(s) in the patient's EMR;altering the patients care plan; reserving resources, for the patient,in the care facility needed to treat and/or manage the predictedmacrophage activation disorder(s); preventing and/or interruptingdischarge orders from being submitted for the patient until such time asa responsible care provider has disabled the automatic dischargeprevention measure(s); ordering increased monitoring of the patient;ordering increased testing of the patient; ordering prescriptions forthe patient; and/or issuing an electronic alert or notification to the aresponsible care provider and/or patient.

Referring now to the drawings in general, and initially to FIG. 1A inparticular, an aspect of an operating environment 100 is providedsuitable for practicing an embodiment of the technologies describedherein. We show certain items in block-diagram form more for being ableto reference something consistent with the nature of a patentspecification than to imply that a certain component is or is not partof a certain device. Similarly, although some items are depicted in thesingular form, plural items are contemplated as well (e.g., what isshown as one data store might really be multiple data-stores distributedacross multiple locations). But showing every variation of each itemmight obscure the invention. Thus for readability, we show and referenceitems in the singular (while fully contemplating, where applicable, theplural).

As shown in FIG. 1 , example operating environment 100 provides anaspect of a computerized system for compiling and/or running aspects ofthis disclosure, which in some embodiments may include collecting andanalyzing unstructured text data from electronic health record(s), whichmay include claims data, to assess the texts as to topical orconcept-oriented expressions they contain that are statistically similarto those associated with various clinical conditions or diagnoses; toidentify which condition- or diagnosis-oriented clusters the presenttexts most closely resemble, if any; and to notify the responsibleclinicians of those determinations, suggesting consideration of thoseconditions or diagnoses as part of the constellation of differentialdiagnoses pertinent to the management of the current patient.

Operating environment 100 is one example of a suitable environment andsystem architecture for implementing an embodiment of the disclosure. Asdescribed above, some embodiments may be implemented as a system,comprising one or more computers and associated network and equipment,upon which a method or computer software application is executed.Accordingly, aspects of the present disclosure may take the form of anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “module” or “system.” Further, themethods of the present disclosure may take the form of a computerapplication embodied in computer readable media having machine-readableapplication software embodied thereon. In this regard, amachine-readable storage media may be any tangible medium that cancontain, or store a software application for use by the computingapparatus.

Computer application software for carrying out operations for steps ofthe methods of the present disclosure may be authored in any combinationof one or more programming languages, including an object-orientedprogramming language such as Java, Python, R, or C++ or the like.Alternatively, the application software may be authored in any or acombination of traditional non-object-oriented languages such as C orFORTRAN. The application may execute entirely on the user's computer asan independent software package, or partly on the user's computer inconcert with other connected co-located computers or servers, or partlyon the user's computer and partly on one or more remote computers, orentirely on a remote computer or collection of computers. In the lattercases, the remote computers may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, via the internet using an Internet ServiceProvider or ISP) or an arbitrary, geographically-distributed, federatedsystem of computers, such as a cloud-based system.

Environment 100 includes one or more electronic health record (EHR)systems, such as hospital EHR system 160, communicatively coupled tonetwork 175, which is communicatively coupled to computer system 120. Insome embodiments, components of environment 100 that are shown asdistinct components may be embodied as part of or within othercomponents of environment 100. For example, EHR systems 160 may compriseone or a plurality of EHR systems such as hospital EHR systems, healthinformation exchange EHR systems, clinical genetics/genomics systems,ambulatory clinic EHR systems, psychiatry/neurology EHR systems,insurance, collections or claims records systems; and may be implementedin computer system 120. Similarly, EHR system 160 may perform functionsfor two or more of the EHR systems (not shown). In an embodiment, EHRsystem 160 includes historical claims data for health services,apportionment data, and related health services financial data.

In some embodiments of the disclosure, sequence itemset mining isperformed using data about a population of patients derived from patientEHR information. In particular, presently certain data warehouses arecreated for purposes of public health and observational researchpurposes and are derived from electronic health records repositories insuch a way that they are de-identified so as to comply with applicableconfidentiality laws and regulations. The Cerner Health Facts™ datawarehouse is such a system that has been curated for more than 15 years.It comprises a large ‘transaction database’ where each entry correspondsto a patient's ‘basket’ (a collection of items recorded or transacted atpoints in time during episodes of care services provisioning in thecontributing health care institutions). Each database entry is orderedby the date-time of the transaction. Transaction sequencing isimplemented by grouping medical events occurring in the same ‘ epoch’for the same patient together into ‘baskets’ and ordering the ‘baskets’of each patient by the date-time stamps where the events occurred. Epochdurations may differ according to the age of the patient, or the acuteor chronic nature of the health conditions that pertain to the patient,or the rate of change of the severity of the health conditions, or otherfactors, Epoch durations may be as short as a few minutes (as incritical care ICU or operating room contexts) or may be as long as 10years or more (as in chronic ambulatory care-sensitive conditions,ACSCs).

Continuing with FIG. 1A, network 175 may comprise the Internet, and/orone or more public networks, private networks, other communicationsnetworks such as a cellular network, or similar network(s) forfacilitating communication among devices connected through the network.In some embodiments, network 175 may be determined based on factors suchas the source and destination of the information communicated overnetwork 175, the path between the source and destination, or the natureof the information. For example, intra-organization or internalcommunication may use a private network or virtual private network(VPN). Moreover, in some embodiments items shown communicatively coupledto network 175 may be directly communicatively coupled to other itemsshown communicatively coupled to network 175.

In some embodiments, operating environment 100 may include a firewall(not shown) between a first component and network 175. In suchembodiments, the firewall may reside on a second component locatedbetween the first component and network 175, such as on a server (notshown), or reside on another component within network 175, or may resideon or as part of the first component.

Embodiments of electronic health record (EHR) system 160 include one ormore data stores of health records, which may be stored on storage 121,and may further include one or more computers or servers that facilitatethe storing and retrieval of the health records. In some embodiments,EHR system 160 may be implemented as a cloud-based platform or may bedistributed across multiple physical locations. EHR system 160 mayfurther include record systems, which store real-time or near real-timepatient (or user) information, such as wearable, bedside, or in-homepatient monitors or sensors, for example.

Example operating environment 100 further includes provideruser/clinician interface 142 communicatively coupled through network 175to an EHR system 160. Although environment 100 depicts an indirectcommunicative coupling between interface 142 and EHR system 160 throughnetwork 175, it is contemplated that an embodiment of interface 142 iscommunicatively coupled to EHR system 160 directly. An embodiment ofinterface 142 takes the form of a user interface operated by a softwareapplication or set of applications on a client computing device such asa personal computer, laptop, smartphone, or tablet computing device. Inan embodiment, the application includes the PowerChart® softwaremanufactured by Cerner Corporation. In an embodiment, the application isa Web-based application or applet. A provider clinician applicationfacilitates accessing and receiving information from a user or healthcare provider about a specific patient, set of patients, or providerclinicians, according to the embodiments presented herein. Embodimentsof interface 142 also facilitates accessing and receiving informationfrom a user or health care provider about a specific patient orpopulation of patients including patient history; health care resourcedata; variables measurements, timeseries, and predictions (includingplotting or displaying the determined outcome and/or issuing an alert)described herein; or other health-related information, and facilitatesthe display of results, recommendations, or orders, for example. In anembodiment, interface 142 also facilitates receiving orders for thepatient from the clinician/user, based on the results of monitoring andpredictions. Interface 142 may also be used for providing diagnosticservices or evaluation of the performance of various embodiments.

As shown in example environment 100, user/clinician interface 142 iscommunicatively coupled through network 175 to EHR 160 and patient datacollection 140. In an embodiment, patient data collection 140communicates via network 175 to computer 120 and/or provider clinicianinterface 142. In an embodiment of patient data collection 140(sometimes referred to herein as patient monitor or patient measuringdevice) comprises one or more sensor components operable to receive oracquire clinical or physiological information about a patient, such asvarious types of physiological measurements, physiological parameters orvariables, or similar clinical information associated with a particularphysical or mental state of the patient, and which may be acquiredperiodically or as one or more time series. In an embodiment, one ormore sensor components of patient data collection 140 may comprise auser-wearable sensor component or sensor component integrated into thepatient's environment. Examples of sensor components of such embodimentof patient data collection 140 include a sensor positioned on anappendage (on or near the user's head, attached to the user's clothing,worn around the user's head, neck, leg, arm, wrist, ankle, finger,etc.); skin-patch sensor; ingestible or sub-dermal sensor; sensorcomponent(s) integrated into the user's living environment (includingthe bed, pillow, or bathroom); and sensors operable with or through asmartphone carried by the user, for example. It is also contemplatedthat the clinical or physiological information about patient, such asthe monitored parameters or variables and/or clinical narrativesregarding the patient, used according to the embodiment of the inventiondisclosed herein may be received from human measurements, humanobservations, or automatically determined by sensors in proximity to thepatient. For example, in one embodiment, a nurse periodically measures apatient's physiological variables, such as blood pressure or othervariables described herein, and enters the measurement and/orobservations via patient data collection 140 or interface 142. Inanother example, a nurse or caregiver enters one or more progress notesfor an in-patient via patient data collection 140 or interface 142.

Examples of physiological parameters or variables received via patientdata collection 140 can include, by way of example and not limitation,heart rate, blood pressure, oxygen saturation (SoO2), central venouspressure, other vital signs, other laboratory or physiologicalparameters described herein, or any type of measureable, determinable,or observable physiological or clinical variable or characteristicassociated with a patient, which in some embodiments may be used forforecasting a future value (of the measured variable, a compositevariable based on one or more measured variables, or other factordetermined at least in part from one or more measured variables) of apatient in order to facilitate clinical decision making. For example, insome embodiments, patient data collection 140 may be used for acquiring,determining, or characterizing (by a human caregiver) other typesphysiological variables such as, muscle activity which might be sensedfrom electromyogram signals, eye movement which might be sensed fromelectro-oculogram signals, or other biometric information. In anembodiment, patient monitor 140 collects raw sensor information andperforms signal processing, such as velocity measurement, forming aphysiological variable decision statistic, cumulative summing, trending,wavelet processing, thresholding, computational processing of decisionstatistics, logical processing of decision statistics, pre-processing orsignal condition, etc., part or all of which may be performed on patientdata collection 140, interface 142, and/or computer system 120.

Example operating environment 100 further includes computer system 120,which may take the form of a server, which is communicatively coupledthrough network 175 to EHR system 160, and storage 121.

Computer system 120 comprises one or more processors operable to receiveinstructions and process them accordingly, and may be embodied as asingle computing device or multiple computing devices communicativelycoupled to each other. In one embodiment, processing actions performedby system 120 are distributed among multiple locations such as one ormore local clients and one or more remote servers, and may bedistributed across the other components of example operating environment100. For example, a portion of computing system 120 may be embodied oninterface 142. In one embodiment, system 120 comprises one or morecomputing devices, such as a server, desktop computer, laptop, ortablet, cloud-computing device or distributed computing architecture, aportable computing device such as a laptop, tablet, ultra-mobile P.C.,or a mobile phone.

Embodiments of computer system 120 include computer software stack 125,which in some embodiments operates in the cloud, as a distributed systemon a virtualization layer within computer system 120, and includesoperating system 129. Operating system 129 may be implemented as aplatform in the cloud, and which is capable of hosting a number ofservices such as 122, 124, 126, and 128. Some embodiments of operatingsystem 129 comprise a distributed adaptive agent operating system.Embodiments of services 122, 124, 126, and 128 run as a local ordistributed stack in the cloud, on one or more personal computers orservers such as system 120, and/or a computing device running interfaces140 and 142. In some embodiments, interface 142 operates in conjunctionwith software stack 125.

In embodiments, variables mapping service 122 and records/documents ETLservice 124 provide services that facilitate retrieving frequent itemsets, extracting database records, and cleaning the values of variablesin records. For example, service 122 may perform functions for synonymicdiscovery, indexing or mapping variables in records, or mappingdisparate health systems' ontologies, such as determining that aparticular medication frequency of a first record system is the same asanother record system. In some embodiments, these services may invokecomputation services 126.

Computation services 126 perform statistical software operations, andinclude statistical calculation packages such as, in one embodiment, theR system (the R-project for Statistical Computing, which supportsR-packages or modules tailored for specific statistical operations, andwhich is accessible through the Comprehensive R Archive Network (CRAN)at http://cran.r-project.org) or similar services, and R-system modulesor packages such as packages fractal, for fractal time series modelingand analysis, and entropy, which includes various estimators fordetermining entropy, or similar program routines or libraries.Computation services 126 also may include natural language processingservices (not shown) such as Discern nCode™ developed by CernerCorporation, or similar services. In an embodiment, computation services126 include the services or routines, which may be embodied as one ormore software agents or routines such as the example embodiments ofcomputer program routines illustratively provided in FIG. 6A. In someembodiments, computation services 126 use EHR 160 and/or model data andmodel storage services 128. Some embodiments of stack 125 may furtheruse Apache Hadoop and Hbase framework (not shown), or similar frameworksoperable for providing a distributed file system, and which in someembodiments facilitate provide access to cloud-based services such asthose provided by Cerner Healthe Intent®. Additionally, some embodimentsof stack 125 may further comprise one or more services stream processingservice(s) (not shown). For example, such stream processing service(s)may be embodied using IBM InfoSphere stream processing platform, TwitterStorm stream processing, Ptolemy or Kepler stream processing software,or similar complex event processing (CEP) platforms, frameworks, orservices, which may include the user of multiple such stream processingservices (in parallel, serially, or operating independently). Someembodiments of the invention also may be used in conjunction with CernerMillennium®, Cerner CareAware® (including CareAware iBus®), CernerCareCompass®, or similar products and services.

Example operating environment 100 also includes storage 121 (or datastore 121), which in some embodiments includes patient data for acandidate or target patient (or information for multiple patients),including raw and processed patient data; variables associated withpatient recommendations; recommendation knowledge base; recommendationrules; recommendations; recommendation update statistics; an operationaldata store, which stores events, frequent itemsets (such as “X oftenhappens with Y”, for example), and item sets index information;association rulebases; agent libraries, solvers and solver libraries,and other similar information including data and computer-usableinstructions; patient-derived data; and health care providerinformation, for example. It is contemplated that the term data includesany information that can be stored in a computer-storage device orsystem, such as user-derived data, computer usable instructions,software applications, or other information. In some embodiments, datastore 121 comprises the data store(s) associated with EHR system 160.Further, although depicted as a single storage data store, data store121 may comprise one or more data stores, or may be in the cloud.

Turning briefly now to FIG. 1B, there is shown one example embodiment ofcomputing system 900 that has software instructions for storage of dataand programs in computer-readable media. Computing system 900 isrepresentative of a system architecture that is suitable for computersystems such as computing system 120. One or more CPUs such as 901, haveinternal memory for storage and couple to the north bridge device 902,allowing CPU 901 to store instructions and data elements in systemmemory 915, or memory associated with graphics card 910, which iscoupled to display 911. Bios flash ROM 940 couples to north bridgedevice 902. South bridge device 903 connects to north Bridge device 902allowing CPU 901 to store instructions and data elements in disk storage931 such as a fixed disk or USB disk, or to make use of network 933 forremote storage. User I/O device 932 such as a communication device, amouse, a touch screen, a joystick, a touch stick, a trackball, orkeyboard, couples to CPU 901 through south bridge 903 as well. Thesystem architecture depicted in FIG. 1B is provided as one example ofany number of suitable computer architectures, such as computingarchitectures that support local, distributed, or cloud-based softwareplatforms, and are suitable for supporting computing system 120.

Returning to FIG. 1A, in some embodiments, computer system 120 is acomputing system made up of one or more computing devices. In someembodiments, computer system 120 includes one or more software agents,and in an embodiment includes an adaptive multi-agent operating system,but it will be appreciated that computer system 120 may also take theform of an adaptive single agent system or a non-agent system. Computersystem 120 may be a distributed computing system, a data processingsystem, a centralized computing system, a single computer such as adesktop or laptop computer or a networked computing system.

With reference now to FIG. 2A, an exemplary method 200 is provided forgenerating one or more gradient boosting models for use in determiningor predicting the occurrence of a macrophage activation syndrome.Embodiments of method 200 implement the discovery that a characteristicaspect of macrophage activation disorders, including HLH, entailsincreased high-frequency variability of various laboratory biomarkersand physiological measurements, reflecting altered dynamics andhomeostatic control of interrelated physiologic and biologic subsystems.Some embodiments of portions of method 200 may be performed using thecomputer program routine illustratively shown in FIG. 6A and may use amodel probability lookup table such as the example depicted in FIG. 6B.

In an embodiment of method 200 a series of a single laboratory biomarkerand/or physiologic measurement may be used. For example, heart rate,systolic blood pressure, ferritin, fibrinogen, LHD, triglyceride,erythrocytes, leukocytes, platelets, albumin, lactate dehydrogenase,creatinine, resting respiratory rate, resting heart rate, or bodytemperature measurements may be used. It will be understood by thoseskilled in the art that the prior example is not an exhaustive list ofthe laboratory biomarker or physiologic measurements contemplated by thedisclosed invention; rather, it is merely an illustrative example and,as such, any laboratory biomarker and/or physiologic measurements may beused in an embodiment of method 200 without departing from the scope ofthis disclosure.

In an embodiment of method 200, a series of at least two laboratorybiomarkers and/or physiologic measurements may be used. For example, atleast two of heart rate, systolic blood pressure, ferritin, fibrinogen,LHD, triglyceride, erythrocytes, leukocytes, platelets, albumin, lactatedehydrogenase, creatinine, resting respiratory rate, resting heart rate,and/or body temperature measurements may be used. It will be understoodby those skilled in the art that the prior example is not an exhaustivelist of the laboratory biomarker or physiologic measurementscontemplated by the disclosed invention; rather, it is merely anillustrative example and, as such, any laboratory biomarker and/orphysiologic measurements may be used in an embodiment of method 200without departing from the scope of this disclosure.

At step 201, receive laboratory and/or physiologic measurements of thepatient and corresponding date-time coordinates. In an embodiment, step201 comprises measuring (or receiving) the most recent laboratory inphysiologic measurement(s) and corresponding date time coordinate(s).Embodiments of step 201 may be facilitated using patient data collectioncomponent 140. Additionally, and/or alternatively, an embodiment of step201 may be facilitated by using EHR 160 and/or any electronic healthrecord communicatively coupled with network 175. As such, an embodimentof step 201 may access/receive laboratory and/or physiologicmeasurements of the patient from healthcare facilities, and/orlaboratories, other than the healthcare facility where the patient iscurrently located. For example, the most recent laboratory orphysiologic measurement and corresponding date time coordinate may be alaboratory or physiologic measurement taken by the patient's primarycare physician at the primary care physician's office and stored in theprimary care physician's electronic health record system. At some laterpoint in time, the patient may be admitted to a hospital wherein anembodiment of method 200 may be performed. At step 201, the laboratoryor physiologic measurement(s) stored in the primary care physicianselectronic health record system may be accessed through network 175.

At step 202, construct a time series of laboratory and/or physiologicmeasurements of the patient. The time-series may comprise previousmeasurement values and their respective measurement date-timecoordinates, and may be received from an operational data store, such asstorage 121. Some embodiments of step 202 further comprise constructinga time series by appending the most recent measurement values determinedin step 201 to the previous time series retrieved in step 202 therebyforming an updated time series.

In an embodiment, step 202 further comprises determining that thetime-series is of sufficient length for the determination of theprobability of macrophage activation syndrome. In such an embodiment,when the length of the time series is determined to be of sufficientlength the method 200 continues to step 203. Otherwise method 200 waitsuntil the next measurement (step 201) is received and appended to thetime series, thereby lengthening it. In an embodiment of step 202, themethod 200 continues to wait, receive, and append measurements untilsuch time as a time-series of sufficient length is created.

In an embodiment of step 202, the time-series is of sufficient lengthwhen it comprises at least two measurements. In an embodiment, the timeseries is of sufficient length when it comprises at least threemeasurements in length. In an embodiment, the time-series comprises atleast three measurements acquired at, at least, 24 hour intervals. Assuch, a first measurement may be acquired at T₀, a second measurementmay be acquired at T₁, wherein T₁≥T₀+24 hours. A third measurement maybe acquired at T₂, wherein T₂≥T₁+24 hours. Additionally, in anembodiment, measurements acquired between the 24 hour intervals may beincluded in the time series so long as at least three measurements areacquired with, at least, 24 hour between measurements. For example, thefirst measurement may be acquired at T₀; no measurement(s) may beacquired between T₀ and T₁; the second measurement may be acquired atT₁, n₁ measurement(s) may be acquired between T₁ and T₂; and, the thirdmeasurement may be acquired at T₂. As such, the time series may includeat least the first measurement, no measurement(s), the secondmeasurement, n₁ measurement(s), and the third measurement.

Additionally, and/or alternatively, in an embodiment, the time intervalsbetween measurements may be the same duration or may be of varyingduration. In an embodiment, the three or more measurements may beacquired within thirty days (e.g., T₀, T₁, and T₂ occur within thirtydays).

Additionally, in some embodiments, the measurements for a givenlaboratory or physiologic parameter may be acquired, at least in part,asynchronously from other laboratory or physiologic parameters. Forexample, measurements for a first parameter may be acquired over a firsttime period; measurements for a second parameter may be acquired over asecond time period; measurements for a third parameter may be acquiredover a third time period; and, measurements for a fourth parameter maybe acquired over a fourth time period. It will be understood by thoseskilled in the art that the various time periods may overlap, includeindividual measurements from the same specimen, or may be identical. Assuch, embodiments of the disclosure are not reliant on laboratory orphysiological measurements made at the same time or with the samepatient specimen.

At step 203, receive one or more predictive models. In an embodiment,the predictive model(s) is generated and trained from a population ofpatients in whom subsequent actual occurrences of macrophage activationsyndrome in a defined future interval are known, as further describedherein. In some embodiments, the models may be stored and received froma reference data store, which may be embodied as storage 121 and/ormodel data and models storage services 128. Similarly, in an embodimentof step 203, method 200 may receive an indication that the one or morepredictive models is available for use and in turn prepare the one ormore time series for transmission. In other words, an embodiment of step203 may receive an indication that one or more predictive models“housed” in a communicatively coupled specially configured computer, isavailable to receive the patient data (e.g. has computational capacityavailable). In such an embodiment, step 203 may further comprisesecurely transmitting the patient data to the one or more predictivemodels for remote analysis.

At step 204, calculate high-frequency components of a power spectrum ora surrogate measurement of the high frequency band of a power spectrum.Embodiments of step 204 calculate high-frequency components of the powerspectrum of each of the one or more time-series, constructed at step202, or a surrogate measure of the high-frequency band of the powerspectrum, such as the root mean square of successive deviations (RMSSD)of each of the one or more time series, constructed at step 202. In someembodiments, step 204 may be facilitated by computation services 126.

At step 205, apply the calculated high-frequency parameters or surrogatehigh-frequency parameters, calculated at step 204, as inputs to thepredictive model(s), received at step 203, to determine the forecastprobability of current and/or future macrophage activation disorders forthe patient. Embodiments of step 205 may calculate the forecastprobabilities of a macrophage activation syndrome, such as HLH, byapplying the parameters above as inputs to predictive model(s)previously determined from a population of patients in whom subsequentactual occurrences of a macrophage activation syndrome such as HLH in adefined future time interval were known. In some embodiments, thecalculated high-frequency parameter or surrogate high-frequencyparameter, from step 204, and the one or more time series, from step202, may be applied as inputs to the predictive model(s) to determinethe forecast probability of current and/or future macrophage activationdisorders for the patient. In some embodiments, the calculatedhigh-frequency parameter or surrogate high-frequency parameter from step204 may be applied as inputs to the predictive model(s) to determine theforecast probability of current and/or future macrophage activationdisorders for the patient. Using the model(s) updated with inputparameters, a calculated classification and/or forecast probability offuture macrophage activation syndrome occurrence within the definedfuture time interval is determined. In some embodiments, step 203 mayuse the computer program routine illustratively shown in FIG. 6A and/ormay use a model probability lookup table such as the example depicted inFIG. 6B.

Turning briefly to FIG. 6B, an exemplary model probability lookup tableis provided. In embodiments, this example lookup table may include thenumber of transgressions 602 detected by the predictive model(s), forexample as shown in the computer program routine shown in FIG. 6A. Inembodiments the number of transgressions 602 may be associated with aforecast probability 604.

Returning to FIG. 2A, at step 206 compare the probability against athreshold. Some embodiments of step 206 compare the calculatedclassification and/or forecast probability calculated at step 205 to athreshold. The forecast probability may be compared to one or morethreshold probabilities, at step 206. The threshold probabilities may bepredetermined by a medical professional or medical care provider or beempirically based. In an embodiment of step 206, the threshold may be apredetermined probability and/or classification. Additionally, in anembodiment of step 206 the threshold may be a predetermined probabilityand/or classification determined based on the patient's underlyingcondition(s) and/or the laboratory or physiologic measurements used toconstruct the time-series. For example, a threshold probability may beestablished at one value for patients with cancer; a thresholdprobability maybe established at a second value for patients with aninfectious disease; a threshold probability maybe established at a thirdvalue for patients with cancer and a comorbid infectious disease;and/or, a threshold probability maybe established at a fourth value forpatients with an autoimmune disease. For another example, a thresholdprobability maybe established for probabilities calculated based on of afirst laboratory or physiological parameter (e.g., ferritin); athreshold may be established for probabilities calculated based on of asecond laboratory or physiologic parameter (e.g., fibrinogen); athreshold may be established for probabilities based on of a thirdlaboratory or physiologic parameter (e.g., heart rate); and so on. Foryet another example, a threshold may be established for probabilitiescalculated based on various combinations of two or more laboratory orphysiologic parameters. It will be understood by those skilled in theart that the above referenced examples represent an illustrative subsetof possible predetermined thresholds and as such are not intended tolimit the scope of this disclosure.

At step 207, determine that the probability threshold is satisfied. Insome embodiments, the probability threshold may be satisfied when, forexample, the forecast probability exceeds the threshold probability. Insome embodiments, the probability threshold may be satisfied when, forexample, the forecast probability equals the threshold probability.

At step 208, based on the determination that the threshold is satisfied,initiate at least one action. Embodiments of step 208, implement method200 in a tangible way by using the results of, at least, step 207 toinitiate at least one action. In an embodiment, the action may benotifying the responsible care provider; placing new order(s) in thepatient's EMR; altering the patient's care plan; reserving resources,for the patient, in the care facility needed to treat and/or manage thepredicted macrophage activation disorder(s); preventing and/orinterrupting discharge orders from being submitted for the patient untilsuch time as a responsible care provider has reviewed method 200'sresults and disabled the automatic discharge prevention measure(s);ordering increased monitoring of the patient; ordering increased testingof the patient; and/or ordering prescriptions for the patient; issuingan electronic alert or notification to the a responsible care providerand/or patient.

Further it will be understood that embodiments of method 200 may includeadditional and/or alternative steps; like those discussed in relation tomethod 209.

With reference now to FIG. 2B, a flow diagram is provided illustratingan exemplary method 209 for generating one or more gradient boostingmodels for use in determining or predicting the occurrence of amacrophage activation syndrome. Embodiments of method 209 implement thediscovery that a characteristic aspect of macrophage activationdisorders, including HLH, entails increased high-frequency variabilityof various laboratory biomarkers and physiologic measures, reflectingaltered dynamics and homeostatic control of interrelated physiologic andbiologic subsystems. Some embodiments of portions of method 209 may beperformed using the computer program routine illustratively shown inFIG. 6A and may use a model probability look-up table such as theexample depicted in FIG. 6B. Some embodiments of method 209 and/or someportions of method 209 may be facilitated by environment 100.

In an embodiment of method 209 a series of a single laboratory biomarkerand/or physiologic measurement may be used. For example, heart rate,systolic blood pressure, ferritin, fibrinogen, LHD, or triglyceridemeasurements may be used. It will be understood by those skilled in theart that prior example is not an exhaustive list of the laboratorybiomarker or physiologic measurements contemplated by the disclosedinvention; rather, it is merely an illustrative example and as such anylaboratory biomarker and/or physiologic measurements may be used in anembodiment of method 209 without departing from the scope of thisdisclosure.

In an embodiment, a series of at least two laboratory biomarkers and/orphysiologic measurements may be used. For example, at least two of heartrate, systolic blood pressure, ferritin, fibrinogen, LHD, andtriglyceride measurements may be used. It will be understood by thoseskilled in the art that prior example is not an exhaustive list of thelaboratory biomarker or physiologic measurements contemplated by thedisclosed invention; rather, it is merely an illustrative example and assuch any laboratory biomarker and/or physiologic measurements may beused in an embodiment of method 209 without departing from the scope ofthis disclosure.

At step 210, determine the patient on whom to perform the macrophageactivation syndrome determination or prediction. In an embodiment, thedetermination may be made automatically based off of predeterminedcriteria, a rule, a rule set, a machine learned model, a predictivealgorithm, and/or the like. As such, an embodiment of method 209 may beused as a pervasive screening tool. For example, at step 210 the systemmay determine it appropriate to perform macrophage activation syndromedetermination or prediction on the entire patient population, or asubpopulation, of a hospital at set intervals, continuously, or as therequisite patient measurements are acquired. For another example, step210 may use patient information such as patient demographics, diagnosis,care plans, medications, or the like to determine whom to perform themacrophage activation syndrome determination or prediction on. In anembodiment, the determination may be made by a user/clinician throughthe user/clinician interface 142. As such, an embodiment of method 209may be used to enhance decision support systems and health care networksto function as a targeted screening or prediction tool.

At step 220, acquire and store serial measurements of laboratory andphysiologic parameters. In one embodiment, step 220 comprises measuring(or receiving) the most recent laboratory and physiologic measurement(s)and corresponding date-time coordinate(s). Embodiments of step 220 maybe facilitated using patient data collector 140. Additionally, and/oralternatively, embodiments of step 220 may be facilitated by using EHR160.

At step 230, retrieve time series from previous measurements to use forcomputation of risk model. The time series may comprise previousmeasurement values and their respective measurement date-timecoordinates, and may be received from an operational data store, such asstorage 121. Some embodiments of step 230 further comprise constructinga time series by appending the most recent measurement values determinedin step 220 to the previous time series retrieved in step 230 therebyforming an updated time series.

At step 240, the length of the time series is determined and if thelength is sufficient, then method 209 continues to step 250. Otherwise,method 209 waits until the next measurement (step 220) is received andappended to the time series, thereby lengthening it. In an embodiment,the time series is at least three measurements in length beforeproceeding to step 250 for further calculation of forecasts, beginningwith computation of the input values required by the statisticalpredictive models.

As discussed in reference to step 202, in an embodiment of step 240 thetime series comprises at least three measurements acquired at, at least,24 hour intervals. Again as discussed in reference to step 202, in anembodiment of step 240 measurements acquired between the 24 hourintervals may be included in the time series so long as at least threemeasurements are acquired with, at least, 24 hour between measurements.

Again as discussed in reference to step 202, in an embodiment of step240 the time intervals between measurements may be the same duration ormay be of varying duration. In an embodiment, the three or moremeasurements are acquired within thirty days (e.g., T₀, T₁, and T₂ occurwithin thirty days). It will be understood by those skilled in the artthat, as discussed above, the time series may comprise previouslyacquired measurements without departing from the scope of the invention.

Additionally, in some embodiments, the measurements for a givenlaboratory or physiologic parameter may be acquired, at least in part,asynchronously from other laboratory or physiologic parameters. Forexample, measurements for a first parameter may be acquired over a firsttime period; measurements for a second parameter may be acquired over asecond time period; measurements for a third parameter may be acquiredover a third time period; and, measurements for a fourth parameter maybe acquired over a fourth time period. It will be understood by thoseskilled in the art that the various time periods may overlap, includeindividual measurements from the same specimen, or may be identical. Assuch, embodiments of the disclosure are not reliant on laboratory orphysiological measurements made at the same time or with the samepatient specimen.

At step 250, determine high frequency spectral density or root meansquare of successive deviations of the time series. Embodiments of step250 calculate high-frequency components of the power spectrum of each ofthe time series, or a surrogate measure of the high-frequency band ofthe power spectrum, such as the root mean square of successivedeviations (RMSSD) of each of the time series. At step 260, in someembodiments, method 209 determines the transgressions of target valueswithin the time series.

At step 270, calculate and store the probabilities of current and/orfuture macrophage activation disorder for the patient. Embodiments ofstep 270 may calculate the probabilities of a macrophage activationsyndrome, such as HLH, by applying the parameters above as inputs topredictive models previously determined from a population of patients inwhom subsequent actual occurrences of a macrophage activation syndromesuch as HLH in a defined future time interval were known. The models maybe stored and received from a reference data store, which may beembodied as storage 121. Using the models updated with input parameters,a calculated classification or forecast probability of future macrophageactivation syndrome occurrence within the defined future time intervalis determined.

At step 275, it is determined whether one or more probability thresholdsis satisfied. Embodiments of step 275 determine whether the forecastprobabilities or classifications received exceed the probabilitythreshold(s) established for issuing an alarm or other signal to thepatient or to the patient's caregivers, or performing another action. Insome embodiments, the thresholds are pre-determined, may be determinedbased on the particular condition (e.g., HLH), or determined based onthe patient, including physiological or demographics information aboutthe patient. In some embodiments of step 275, the probability thresholdis determined as discussed in reference with step 206.

If the threshold(s) are not transgressed, then method 209 proceeds tostep 280, and information is provided indicating that the patient hasnormal status. If one or more of the thresholds are transgressed, thenmethod 280 proceeds to step 290 and a recommendation is determined orretrieved. Step 290 may provide a specific recommended treatment ortherapy, or simply a notification of the prediction or detection, or acombination. In an embodiment, step 290 comprises determining a type ofsignal(s) to emit to the patient or other caregiver(s) [ordinal indiciarepresenting ‘increased likelihood’ vs. ‘likelihood not increased’ orother categories; numeric probability; trend display; other], and/ordetermining whether the signal(s) to be emitted should be accompanied byspecific therapy adjustment advice. Some embodiments of step 290 furtherinclude combining a signal and therapy adjustment advice (if applicable)to create a personalized advisory message.

Additionally, and/or alternatively, some embodiments of step 290implement the aforementioned discovery by performing an action. In suchan embodiment, the action may be placing new order(s) in the patient'sEMR; altering the patients care plan; reserving, for the patient,resources in the care facility needed to treat and/or manage thepredicted macrophage activation disorder(s); preventing and/orinterrupting discharge orders from being submitted for the patient untilsuch time as a responsible care provider has reviewed method 209'sresults and disabled the automatic discharge prevention measure(s);and/or, ordering increased monitoring of the patient; ordering increasedtesting of the patient. In an embodiment, the specific action(s)implemented may be based on the number of determined thresholdtransgressions.

At step 295, the determined risk level and indicated recommendation(s)(if applicable) are communicated. Some embodiments of step 295 maycommunicate a message to the patient and/or other caregivers, and mayfurther store the result of the clinical calculation and communicationin an electronic health record associated with the patient. In someembodiments, the patient's laboratory and/or physiologic measurements,ordered care plan, actually carried out care plan,occurrence/nonoccurrence of a macrophage activation disorder, outcome,and/or any other relevant data may be monitored and incorporated intothe one or more predictive models; thereby, in an iterative processes,improving the performance of the one or more predictive models.

Further it will be understood that embodiments of method 209 may includeadditional and/or alternative steps; like those discussed in relation tomethod 200. As such, the application of embodiments of method 200 and/or209 may result in a tangible, anticipatory change in the patient's orpatients' care that prior methods and systems were incapable ofachieving. Thus, by employing the techniques described herein,embodiments can overcome the deficiencies that are associated with theconventional industry practice by gathering particularized data fromunique data sources and relying on unconventional techniques to overcomethe tragically fatal deficiencies found within prior industry practice.

Example Reduction to Practice

With reference to FIGS. 3A-5D, and continuing reference to the drawings,an example embodiment reduced to practice is now described. Reduction topractice was accomplished using a computer running the Linux operatingsystem (operating system 129), the open-source statistical softwarepackage R (software services 126), and the R modules (packages) entropyand fractal.

For the reduction to practice, an observational study of was performedusing a consented, secondary-use-rights-granted data set. Illustrativeseries of self-monitoring glucometer values were retrieved from a subsetof 1,667 HLH patients whose de-identified, confidentiality-protectedhealth records were stored and maintained in Cerner's Health Facts® datawarehouse. The cohort selected was comprised of 124 HLH patients forwhom Health Facts® contained sufficient serial laboratory andphysiologic values measured over a period of not less than 3 days butnot more than 180 days.

The ranges of values of the RMSSD3 for 3-sample-wide time series of ameasured quantity x are calculated as:

$\begin{matrix}{{RNSS{D_{3}(x)}} = \sqrt{\frac{1}{N - 1}\left( {{\sum}_{i = 1}^{N - 1}\left( {x_{i + 1} - x_{i}} \right)^{2}} \right)}} & \left( {{Eq}.1} \right)\end{matrix}$

where N=3.

FIGS. 3A-3D shows a statistical distribution of values of the root meansquare of successive differences for the 3-sample-wide time series forone of the laboratory parameters used for determining predictive modelsfor HLH, and FIGS. 4A-4D depict RMSSD distributions.

The practice of the embodiments described herein allows latitude withregard to selecting the length of the time series upon which theforecasts are to be based. In this example embodiment actually reducedto practice, it was determined that time series that are shorter thanapproximately 3 samples do not contain enough representation of thevariability or spectral content to yield adequate predictive accuracyfor the intended purpose of assisting in determining or forecasting thepresence of a macrophage activation syndrome such as HLH. It was furtherdetermined that, while incremental accuracy continued to increase fortime series that were longer than 10 samples, the disadvantages of theextra time series length in terms of withholding risk predictions untilthe accrual of time series 10 or more samples long outweighed thebenefits accompanying the incremental accuracy gains.

Furthermore, the practice of some embodiments allows latitude withregard to the length of time into the future (or the number of samplesto be measured at a series of future times that will constitute a sampletime series appendment to the existing already-accrued time series) forwhich the forecasts shall pertain. In these embodiments, where thatfuture interval is shortened, then the likelihood of excursions outsidethe target range may be small, and may be reasonably well-fitted by aPoisson or negative binomial distribution. If the future interval islengthened beyond 30 days or so, then the likelihood of excursionsoutside the target range becomes very large, such that the prediction isconstantly high and is of little utility in a clinician'sdecision-making. In the example reduction to practice, a forecastingtime horizon of 3 to 6 samples was determined to be an effectivecompromise between value to the patient and the mutually-competing goalsof statistical sensitivity and specificity.

Choice of suitable thresholds for the predicted probabilities ofmacrophage activation syndromes is not only a matter of selecting thetarget range and determining the mathematical/statistical model for thattarget range from a suitably large cohort of patients of a particulartype and severity. Instead, it is also and jointly a matter of selectingthe decision-level based on examination of receiver operatingcharacteristic (ROC) curves or Fl statistics or similar means forbalancing true-positive against false-positive classification rates.FIGS. 5A and 5B depict max shock index (SI) distribution in HLH patientsand the number of threshold transgressions as a function of the max SIindex, respectively.

False-positive (Type I) errors and persistence (delayed offsetting) ofpositive signals beyond the period when true-positive events are likelyare, as with any predictive model, undesirable. However, in the contextof clinical decision support for HLH diagnosis a modest frequency offalse-positives and modest persistence of positivity are not severefaults. Moderate persistence of a positive signal serves to encouragegreater vigilance on the part of the clinician. Indeed, there may be afavorable psychology to occasional false-positives or short persistenceof positive signals. In other words, high statistical sensitivity in theclassification-prediction use-case that is the object of this inventionis a virtue.

By contrast, high specificity is less important for this use-case.False-negative (Type II) errors and premature offsetting of positivesignals are much to be avoided insofar as they convey false assurance ofsafety and may wrongly encourage the treating clinicians to be lessvigilant than usual (with regard to monitoring of laboratory tests andphysiologic variables, adjustment of medications, and administration ofblood products or other biologics when indicated). In this way falseassurances emitted by a predictive model system and method can beassociated with untoward delays in diagnosing macrophage activationsyndromes such as HLH. FIGS. 5C and 5D depict a Q-Q plot of copularegression and the accuracy of the predicted threshold transgressions,respectively.

Many different arrangements of the various components depicted, as wellas components not shown, are possible without departing from the spiritand scope of the present invention. Embodiments of the present inventionhave been described with the intent to be illustrative rather thanrestrictive. Alternative embodiments will become apparent to thoseskilled in the art that do not depart from its scope. A skilled artisanmay develop alternative means of implementing the aforementionedimprovements without departing from the scope of the present invention.

It will be understood that certain features and subcombinations are ofutility and may be employed without reference to other features andsubcombinations and are contemplated within the scope of the claims. Notall steps listed in the various figures need be carried out in thespecific order described. Accordingly, the scope of the invention isintended to be limited only by the following claims.

What is claimed is:
 1. A method comprising: receiving one or moremeasurements of a subject, the one or more measurements comprising: oneor more first laboratory or physiologic measurements and one or morecorresponding date-time coordinates; and one or more second laboratoryor physiologic measurements and a respective date-time coordinate, theone or more second laboratory or physiologic measurements being lessrecent than the one or more date-time coordinates corresponding to theone or more first laboratory or physiologic measurements; retrieving atime series including at least the one or more first laboratory orphysiologic measurements and the one or more second laboratory orphysiologic measurements, the time series constructed based on the oneor more corresponding date-time coordinates of the one or more firstlaboratory or physiologic measurements and the respective date-timecoordinate of the one or more second laboratory or physiologicmeasurements; determining a probability of a macrophage activationsyndrome for the subject by determining, for the time series,high-frequency components of a power spectrum or a surrogate measure ofa high-frequency band of the power spectrum, the probability determinedby using one or more predictive models; determining that the probabilityof the macrophage activation syndrome exceeds a threshold associatedwith a condition of the subject, with the one or more first laboratoryor physiologic measurements, with the one or more second laboratory orphysiologic measurements, or with other laboratory or physiologicmeasurements; in response to determining the probability of themacrophage activation syndrome exceeds the threshold, identifying arecommendation for a treatment or a therapy for the subject; andtransmitting a notification to a caregiver indicating that the subjecthas the probability, corresponding to a future time interval, of themacrophage activation syndrome exceeding the threshold, the notificationcomprising the recommendation.
 2. The method of claim 1, wherein thesurrogate measure of the high-frequency band of the power spectrumcomprises a root mean square of successive deviations (RMS SD).
 3. Themethod of claim 1, wherein the threshold is pre-determined, specific tothe subject, or established for issuing an alarm to the subject or thecaregiver, and wherein the threshold comprises one or more thresholds ora range.
 4. The method of claim 1, wherein the one or more predictivemodels are previously determined from a population of subjects in whomsubsequent actual occurrences of the macrophage activation syndrome in adefined time interval were known, and wherein the future time intervalis based on the one or more predictive models.
 5. The method of claim 1further comprising: upon emitting the notification, monitoring at leastthe one or more first laboratory or physiologic measurements and adiagnosis of the subject; determining, at least partially based onmonitoring the one or more first laboratory or physiologic measurementsand the diagnosis, a status of the macrophage activation syndrome;generating an updated one or more predictive models based on the statusof the macrophage activation syndrome, the one or more first laboratoryor physiologic measurements, the diagnosis, and the one or morepredictive models; and redetermining the high-frequency components ofthe power spectrum or the surrogate measure of the high-frequency bandof the power spectrum by using the updated one or more predictivemodels.
 6. The method of claim 1, further comprising at least one of:placing a new orders in an electronic medical record (EMR) of thesubject; modifying a care plan of the subject; reserving resources, forthe subject, in a care facility needed to treat the macrophageactivation syndrome; instituting automatic discharge prevention measurescomprising preventing and interrupting discharge orders from beingsubmitted for the subject until such time as the caregiver has reviewedthe notification and disabled the automatic discharge preventionmeasures; ordering increased monitoring of the subject; and orderingincreased testing of the subject or ordering prescriptions for thesubject.
 7. The method of claim 1, further comprising: monitoring adiagnosis of the subject; determining, based on the diagnosis, anoccurrence or nonoccurrence of the macrophage activation syndrome; andupdating, based on the occurrence of the macrophage activation syndrome,the one or more predictive models.
 8. A system comprising: a processor;and a computer storage medium storing computer-usable instructions that,when used by the processor, cause the processor to perform operationscomprising: receive one or more measurements of a subject, the one ormore measurements comprising: one or more first laboratory orphysiologic measurements and one or more corresponding date-timecoordinates; and one or more second laboratory or physiologicmeasurements and a respective date-time coordinate, the one or moresecond laboratory or physiologic measurements being less recent than theone or more date-time coordinates corresponding to the one or more firstlaboratory or physiologic measurements; retrieve a time series includingat least the one or more first laboratory or physiologic measurementsand the one or more second laboratory or physiologic measurements, thetime series based on the one or more corresponding date-time coordinatesof the one or more first laboratory or physiologic measurements and therespective date-time coordinate of the one or more second laboratory orphysiologic measurements; determine a probability of a macrophageactivation syndrome for the subject by determining, for the time series,high-frequency components of a power spectrum or a surrogate measure ofa high-frequency band of the power spectrum, the probabilitydeterminable by using one or more predictive models; determine that theprobability of the macrophage activation syndrome exceeds a thresholdassociated with a condition of the subject, with the one or more firstlaboratory or physiologic measurements, with the one or more secondlaboratory or physiologic measurements, or with other laboratory orphysiologic measurements; in response to determining the probability ofthe macrophage activation syndrome exceeds the threshold, identify arecommendation for a treatment or a therapy for the subject; andtransmit a notification to a caregiver indicating that the subject hasthe probability, corresponding to a future time interval, of themacrophage activation syndrome exceeding the threshold, the notificationcomprising the recommendation.
 9. The system of claim 8, wherein theoperations further comprise, based on the probability satisfying thethreshold, perform an action comprising at least one of: placing a neworder in an electronic medical record (EMR) of the subject; altering acare plan of the subject; reserving a resource, for the subject, in acare facility for treating the macrophage activation syndrome; modifyinga plan of care for the subject that prevents, interrupts, or delaysdischarge orders from being submitted or carried out; automaticallyscheduling increased monitoring of the subject; ordering increasedtesting of the subject; automatically scheduling a consultation with acare provider; automatically issuing a clinical order for the subject;or issuing an electronic alert or notification to the care provider orthe subject.
 10. The system of claim 8, wherein the computer storagemedium stores maximum and minimum values defining target ranges for theone or more first laboratory or physiologic measurements.
 11. The systemof claim 8, wherein the operations further comprise: determine aplurality of future values representative of a corresponding pluralityof expected levels of the subject, and cause a display of the pluralityof future values in graphical form on a computing device.
 12. The systemof claim 8, wherein the operations further comprise calculate a Hurstexponent, fractal dimension, or other self-similarity metrics of the atleast one time series, and calculate an entropy or chaotic variabilityof the at least one time series.
 13. The system of claim 8, wherein theoperations further comprise receive the one or more predictive modelsthat are previously determinable from a population of subjects in whomsubsequent actual occurrences of the macrophage activation syndrome in adefined future time interval were known.
 14. The system of claim 8,wherein the one or more first laboratory or physiologic measurementscomprise at least one of levels of: ferritin, triglycerides, fibrinogen,erythrocytes, leukocytes, platelets, albumin, lactate dehydrogenase(LDH), creatinine, resting respiratory rate, resting heart rate,systolic blood pressure, or body temperature.
 15. The system of claim14, wherein the at least one of the levels of the LDH are from a sampleof the subject, and wherein a serial serum level of the LDH is 250 U/Lor greater.
 16. The system of claim 14, wherein a serial serum level ofthe ferritin in a sample of the subject is 150 ng/mL or greater.
 17. Thesystem of claim 14, wherein a serial serum level of the fibrinogen is200 mg/dL or less, and wherein a level of the triglycerides is 180 mg/dLor greater.
 18. The system of claim 14, wherein: RMSSD3 (LDH) is greaterthan 12 U/L/day, at least one LDH level included in a calculation isgreater than 250 U/L; RMSSD3 (ferritin) is greater than 15 ng/mL/day,and at least one Ferritin level included in the calculation is greaterthan 150 ng/mL; RMSSD3 (fibrinogen) is greater than 10 mg/dL/day, and atleast one Fibrinogen level included in the calculation is less than 200mg/dL; RMSSD3 (triglycerides) is greater than 18 mg/dL/day, and at leastone Triglycerides level included in the calculation is greater than 180mg/dL; and wherein specimens utilized for determination of the levelsand RMSSD3 values are separated by not less than 24 hours and not morethan 30 days.
 19. A non-transitory computer-readable medium comprisinginstructions executable by a processor that, when executed, cause theprocessor to perform operations comprising: receiving one or moremeasurements of a subject, the one or more measurements comprising: oneor more first laboratory or physiologic measurements and one or morecorresponding date-time coordinates; and one or more second laboratoryor physiologic measurements and a respective date-time coordinate, theone or more second laboratory or physiologic measurements being lessrecent than the one or more date-time coordinates corresponding to theone or more first laboratory or physiologic measurements; retrieving atime series including at least the one or more first laboratory orphysiologic measurements and the one or more second laboratory orphysiologic measurements, the time series based on the one or morecorresponding date-time coordinates of the one or more first laboratoryor physiologic measurements and the respective date-time coordinate ofthe one or more second laboratory or physiologic measurements;determining a probability of a macrophage activation syndrome for thesubject by determining, for the time series, high-frequency componentsof a power spectrum or a surrogate measure of a high-frequency band ofthe power spectrum, the probability determinable by using one or morepredictive models; determining that the probability of the macrophageactivation syndrome exceeds a threshold associated with a condition ofthe subject, with the one or more first laboratory or physiologicmeasurements, with the one or more second laboratory or physiologicmeasurements, or with other laboratory or physiologic measurements; inresponse to determining the probability of the macrophage activationsyndrome exceeds the threshold, identifying a recommendation for atreatment or a therapy for the subject; and transmitting a notificationto a caregiver indicating that the subject has the probability,corresponding to a future time interval, of the macrophage activationsyndrome exceeding the threshold, the notification comprising therecommendation.
 20. The non-transitory computer-readable medium of claim19, wherein the probability satisfies the one or more thresholds whenthe probability equals or exceeds the one or more thresholds.